Which fields of science are advancing the fastest?

[Exapno_Mapcase]

The science that’s advancing fastest is whatever the newest one is.

It’s like the difference between a start-up that can increase revenues at hundreds of per cent a year vs. Google which creates more money every minute that the start-up does in a year but the percentage increase is far less.

[lazybratsche]

@Wesley_Clark

Supposedly a lab grown hamburger made with cultured meat cost $325,000 in 2013, but the price was only $11 in 2015.

However I don’t think the price has declined much in the last two years. I’m not sure if the price decline was due to new technologies or just economies of scale of existing techniques and technologies.

Eh, that price decrease was probably due to some relatively simple optimization and scaling up from standard small-scale cell culture techniques. Standard lab techniques for growing cells require a lot of expensive labor and consumables. You can save a ton of money by replacing $1000/liter serum of an unborn calf with simpler media (soy broth with a bit of insulin?). Plus, you’re going to save a ton by growing the muscle cells in a big 100 L reactor tended by a $10/hour technician, instead of having a team of PhD scientists laboriously pipette a bunch of 10 mL flasks.

I’m sure there was some ingenuity involved, but I bet anyone used to working with biologic-production-scale cell cultures could figure out how to make cheapish vat meat.

[Wesley_Clark]

@lazybratsche

Eh, that price decrease was probably due to some relatively simple optimization and scaling up from standard small-scale cell culture techniques. Standard lab techniques for growing cells require a lot of expensive labor and consumables. You can save a ton of money by replacing $1000/liter serum of an unborn calf with simpler media (soy broth with a bit of insulin?). Plus, you’re going to save a ton by growing the muscle cells in a big 100 L reactor tended by a $10/hour technician, instead of having a team of PhD scientists laboriously pipette a bunch of 10 mL flasks.

I’m sure there was some ingenuity involved, but I bet anyone used to working with biologic-production-scale cell cultures could figure out how to make cheapish vat meat.

That was my assumption, because prices haven’t declined much from 2015. So I assumed they just started using existing technologies to make the product cheaper.

[ZurBob]

@Stranger_On_A_Train

Purely in terms of the volume of data being acquired, astronomy is growing so rapidly that we have more yottabytes of data coming in than all astronomers currently working have time to process and review. We literally have enough of a data backlog right now to keep the current working population of astronomers busy for decades, and that in an era where we are at a reduction of astronomical facilities, especially space telescopes (though we are getting near optimal use of facilities as a whole).

However, the data from genomics will eclipse that (if it hasn’t already) with the ability to sequence not just genomes of known species but to make differential comparisons of genomes of individual organisms. We will soon be running into some thermodynamic problems with storing and accessing all of the data from genomics using conventional data storage techniques, which are not fast enough to store and retrieve the volumes of data required to perform useful analysis.

We currently have the paradigm of “Big Data”, which doesn’t just mean having to handle a lot of data but find new ways of performing analysis using non-frequentist (generally referred to as Bayesian) sampling systems just to try to understand what the data means. It is as if instead of just looking at ocean waves and calculating frequency statistics to determine significant wave height, we now have to look at each individual wave and tease out the underlying patterns even though they are constantly changing and fluctuating in nonlinear fashion, a task that is impossible using conventional counting methods. But now we’re dealing with such a volume of data (what I term “Colossal Data”) that the time and energy it takes to just handle and process the data is overwhelming, requiring even more advanced methods of hyperparallel processing (dividing parallel processes into different classes or methods to more fully optimize processing effort) and using quantum superposition principles to find best fit solutions that conventional analysis methods using linear transforms or filters would take lifetimes to do. Stranger

Sounds overwhelming. Is it possible this overload is self inflicted by our ability to collect unnecessary data points?

[Darren_Garrison]

@ZurBob

Sounds overwhelming. Is it possible this overload is self inflicted by our ability to collect unnecessary data points?

Sure. So the solution is to gather all astronomers together and get them to agree on which data is necessary and which isn’t. That should go smoothly.

[LSLGuy]

They’re also still at the stage where just because we don’t have a use for the data today doesn’t mean we won’t find a use for it tomorrow.

Imagine we had really good well-calibrated worldwide ocean and air temperature data going back 200 years instead of just 75ish? But who knew in 1880 that that data would be important by 2000?

[Buck_Godot]

I admit I may be biased because this is my field but I’m going to second molecular genetic technology. I started working in it about 15 years ago just before they finished sequencing the human genome, and since then every two years or so there is a new technology that totally revolutionizes how investigations are done. I coauthored a book in 2004 about microarray technology which had a cover price of $149.00 and is retailing for $6.49 due to being woefully out of date.